fix representative tweets

app.py

@@ -380,6 +380,7 @@ def get_topic_value(row, i):
         except Exception as e:
             print(e)
 
+def reprsentative_tweets():
     global top_tweets
     top_tweets = []
     for i in range(len(topic_clusters)):
@@ -393,6 +394,7 @@ def get_topic_value(row, i):
         top_tweets.append(rep_tweets[:5])
         # print('Topic ', i)
         # print(rep_tweets[:5])
+    return top_tweets
 
 def topic_summarization(topic_groups):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -526,6 +528,7 @@ def main(dataset, model):
         base_lda()
         coherence = hyperparameter_optimization()
         topic_assignment(df)
+        top_tweets = reprsentative_tweets()
     else:
         base_bertopic()
         optimized_bertopic()